A Survey on Data Synthesis and Augmentation for Large Language Models

The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation. However, the growth rate of high-quality data is significantly outpaced by the expansion of training datasets, leading to a looming data exhaustion crisis. This underscores the urgent need to enhance data efficiency and explore new data sources. In this context, synthetic data has emerged as a promising solution. Currently, data generation primarily consists of two major approaches: data augmentation and synthesis. This paper comprehensively reviews and summarizes data generation techniques throughout the lifecycle of LLMs, including data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications. Furthermore, We discuss the current constraints faced by these methods and investigate potential pathways for future development and research. Our aspiration is to equip researchers with a clear understanding of these methodologies, enabling them to swiftly identify appropriate data generation strategies in the construction of LLMs, while providing valuable insights for future exploration.

Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation

Zero-shot Panoptic Segmentation (ZPS) aims to recognize foreground instances and background stuff without images containing unseen categories in training. Due to the visual data sparsity and the difficulty of generalizing from seen to unseen categories, this task remains challenging. To better generalize to unseen classes, we propose Conditional tOken aligNment and Cycle trAnsiTion (CONCAT), to produce generalizable semantic vision queries. First, a feature extractor is trained by CON to link the vision and semantics for providing target queries. Formally, CON is proposed to align the semantic queries with the CLIP visual CLS token extracted from complete and masked images. To address the lack of unseen categories, a generator is required. However, one of the gaps in synthesizing pseudo vision queries, ie, vision queries for unseen categories, is describing fine-grained visual details through semantic embeddings. Therefore, we approach CAT to train the generator in semantic-vision and vision-semantic manners. In semantic-vision, visual query contrast is proposed to model the high granularity of vision by pulling the pseudo vision queries with the corresponding targets containing segments while pushing those without segments away. To ensure the generated queries retain semantic information, in vision-semantic, the pseudo vision queries are mapped back to semantic and supervised by real semantic embeddings. Experiments on ZPS achieve a 5.2% hPQ increase surpassing SOTA. We also examine inductive ZPS and open-vocabulary semantic segmentation and obtain comparative results while being 2 times faster in testing.

CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation

Existing Generalized Zero-shot Semantic Segmentation (GZLSS) methods apply either finetuning the CLIP paradigm or formulating it as a mask classification task, benefiting from the Vision-Language Models (VLMs). However, the fine-tuning methods are restricted with fixed backbone models which are not flexible for segmentation, and mask classification methods heavily rely on additional explicit mask proposers. Meanwhile, prevalent methods utilize only seen categories which is a great waste, i.e., neglecting the area exists but not annotated. To this end, we propose CLIPTeacher, a new learning framework that can be applied to various per-pixel classification segmentation models without introducing any explicit mask proposer or changing the structure of CLIP, and utilize both seen and ignoring areas. Specifically, CLIPTeacher consists of two key modules: Global Learning Module (GLM) and Pixel Learning Module (PLM). Specifically, GLM aligns the dense features from an image encoder with the CLS token, i.e., the only token trained in CLIP, which is a simple but effective way to probe global information from the CLIP models. In contrast, PLM only leverages dense tokens from CLIP to produce high-level pseudo annotations for ignoring areas without introducing any extra mask proposer. Meanwhile, PLM can fully take advantage of the whole image based on the pseudo annotations. Experimental results on three benchmark datasets: PASCAL VOC 2012, COCO-Stuff 164k, and PASCAL Context show large performance gains, i.e., 2.2%, 1.3%, and 8.8%